MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models
| العنوان: | MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models |
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| المؤلفون: | Douglas R. M. Azevedo, Marcos O. Prates, Dipankar Bandyopadhyay |
| المصدر: | Journal of Agricultural, Biological and Environmental Statistics. 26:464-491 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | 0106 biological sciences, Statistics and Probability, Multivariate statistics, Computer science, Applied Mathematics, Confounding, Centroid, Context (language use), Random effects model, 010603 evolutionary biology, 01 natural sciences, Agricultural and Biological Sciences (miscellaneous), Synthetic data, Correlation, 010104 statistics & probability, Statistics, Spatial ecology, 0101 mathematics, Statistics, Probability and Uncertainty, General Agricultural and Biological Sciences, General Environmental Science |
| الوصف: | Exploring spatial patterns in the context of disease mapping is a decisive approach to bring evidence of geographical tendencies in assessing disease status and progression. In most cases, multiple count responses (corresponding to disease incidences of multiple types, such as cancer in men and women) are recorded at each spatial location, which may exhibit similar spatial patterns in addition to disease-specific patterns. These are typically modeled using multivariate shared component models, where the spatial (random) effects may be shared between the disease types to model their association. However, this framework is not immune to spatial confounding, where the latent correlation between the spatial random effects and the fixed effects often leads to misleading interpretation. A recent approach to attenuate spatial confounding is the “SPatial Orthogonal Centroid ‘K’orrection”, aka SPOCK, which displaces the geographical centroids, ensuring orthogonality of the spatial random effects and the fixed effects. In this paper, we introduce MSPOCK, or Multiple SPOCK, to tackle spatial confounding for the multiple counts scenario. The methodology is evaluated on synthetic data, and illustrated via an application to new cases of respiratory system cancer for men and women for the US state of California in 2016. Our studies show that the MSPOCK correction leads to a reduction of the posterior variance estimates of model parameters, while maintaining the interpretation of the model. |
| تدمد: | 1537-2693 1085-7117 |
| DOI: | 10.1007/s13253-021-00451-5 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::8d2a3bd05026d877172dd898fa497613 https://doi.org/10.1007/s13253-021-00451-5 |
| Rights: | CLOSED |
| رقم الانضمام: | edsair.doi...........8d2a3bd05026d877172dd898fa497613 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15372693 10857117 |
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| DOI: | 10.1007/s13253-021-00451-5 |